class_docstr: Optimizer that implements the Adagrad algorithm.
  
  Adagrad is an optimizer with parameter-specific learning rates,
  which are adapted relative to how frequently a parameter gets
  updated during training. The more updates a parameter receives,
  the smaller the updates.
  
  Args:
      learning_rate: A float, a
          `keras.optimizers.schedules.LearningRateSchedule` instance, or
          a callable that takes no arguments and returns the actual value to
          use. The learning rate. Defaults to `0.001`. Note that `Adagrad`
          tends to benefit from higher initial learning rate values compared
          to other optimizers. To match the exact form in the original paper,
          use `1.0`.
      initial_accumulator_value: Floating point value. Starting value for the
          accumulators (per-parameter momentum values). Must be non-negative.
      epsilon: Small floating point value for maintaining numerical stability.
      {{base_optimizer_keyword_args}}
  
  Reference:
  
  - [Duchi et al., 2011](
      http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
code: def get_config(self):
      config = super().get_config()
  
      config.update(
          {
              "initial_accumulator_value": self.initial_accumulator_value,
              "epsilon": self.epsilon,
          }
      )
      return config
  
file_path: keras/keras/optimizers/adagrad.py
imports: from keras import initializers
  from keras import ops
  from keras.api_export import keras_export
  from keras.optimizers import optimizer
label_desc: Use image node_id 4 for calling the Adagrad obj's underlying member method code with example usage: obj.get_config() and returns: config